TY - GEN
T1 - Multi-target tracking using proximity sensors
AU - He, Ting
AU - Bisdikian, Chatschik
AU - Kaplan, Lance
AU - Wei, Wei
AU - Towsley, Don
PY - 2010
Y1 - 2010
N2 - We consider the problem of tracking multiple moving targets in a continuous field using proximity sensors, which are binary sensors that can sense target presence by performing local energy detection subject to noise. Compared with more sophisticated sensors, proximity sensors have the advantage of having lower costs and lower energy consumption, but also the disadvantage of being less accurate. In this paper, we propose a hybrid tracking scheme where a coarse-scale tracking is first performed by proximity sensors to narrow down the areas of interest, and then a fine-scale tracking is performed by high-end sensors to estimate the exact target locations, with our focus on the former. In contrast to classic multi-target tracking which assumes 1-1 association between measurements and targets, we show that proximity measurements do not have such association and thus require a different objective. Formulating the coarse-scale tracking as a problem of tracking the histograms of targets in a cell-partitioned field, we develop both an optimal and two approximate solutions via Bayesian Filtering (BF). In particular, one of our approximate solutions decouples the tracking of different targets and thus reduces the dimensionality of BF by relaxing the likelihood function, and the other further reduces the problem into discrete space by quantizing the target mobility model and the relaxed likelihood function. Together with the optimal solution, they provide flexible tradeoffs between accuracy and complexity. Simulations show that the proposed solutions can effectively track targets to the accuracy of a cell and thus reduce uncertainty for the fine-scale tracking.
AB - We consider the problem of tracking multiple moving targets in a continuous field using proximity sensors, which are binary sensors that can sense target presence by performing local energy detection subject to noise. Compared with more sophisticated sensors, proximity sensors have the advantage of having lower costs and lower energy consumption, but also the disadvantage of being less accurate. In this paper, we propose a hybrid tracking scheme where a coarse-scale tracking is first performed by proximity sensors to narrow down the areas of interest, and then a fine-scale tracking is performed by high-end sensors to estimate the exact target locations, with our focus on the former. In contrast to classic multi-target tracking which assumes 1-1 association between measurements and targets, we show that proximity measurements do not have such association and thus require a different objective. Formulating the coarse-scale tracking as a problem of tracking the histograms of targets in a cell-partitioned field, we develop both an optimal and two approximate solutions via Bayesian Filtering (BF). In particular, one of our approximate solutions decouples the tracking of different targets and thus reduces the dimensionality of BF by relaxing the likelihood function, and the other further reduces the problem into discrete space by quantizing the target mobility model and the relaxed likelihood function. Together with the optimal solution, they provide flexible tradeoffs between accuracy and complexity. Simulations show that the proposed solutions can effectively track targets to the accuracy of a cell and thus reduce uncertainty for the fine-scale tracking.
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U2 - 10.1109/MILCOM.2010.5680242
DO - 10.1109/MILCOM.2010.5680242
M3 - Conference contribution
AN - SCOPUS:79951588610
SN - 9781424481804
T3 - Proceedings - IEEE Military Communications Conference MILCOM
SP - 1777
EP - 1782
BT - 2010 IEEE Military Communications Conference, MILCOM 2010
T2 - 2010 IEEE Military Communications Conference, MILCOM 2010
Y2 - 31 October 2010 through 3 November 2010
ER -